Robust finite-time PID control for discrete-time large-scale interconnected uncertain system with discrete-delay

Tharanidharan, V.; Sakthivel, R.; Ren, Yong; Anthoni, S. Marshal

doi:10.1016/j.matcom.2021.08.024

Cited by 14 publications

(10 citation statements)

References 29 publications

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“…Remark 2. For general systems [29,30], the form e(k) = y(k − 1) + e(k − 1) was usually adopted as the integral part of the PID controller. For positive systems, such a form will lead to some obstacles in dealing with the positivity and stability of the systems.…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, it is verified that co-positive Lyapunov functions and linear programming are powerful for the PID of positive systems. As stated in Remark 2, the traditional approach in [27][28][29][30] are not suitable for the PID of positive systems. On one hand, the integral part is modified.…”

Section: Resultsmentioning

confidence: 99%

“…Remark 6. In [27][28][29][30], linear matrix inequality approach was adopted to study the PID control of general systems (non-positive systems). Applying existing approach to positive systems will increase the complexity of the corresponding conditions.…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, a small tuning parameter 𝛼 is introduced to the integral part of PID controller to achieve the positivity and stability of the considering systems. Such a design strategy is different from the traditional PID control design in [27][28][29][30] and has advantages in dealing with the PID control of positive systems.…”

Section: Resultsmentioning

confidence: 99%

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Linear programming‐based proportional‐integral‐derivative control of positive systems

Zhou

Zhang

Jia

et al. 2023

IET Control Theory & Appl

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This paper investigates the proportional‐integral‐derivative (PID) control of positive systems in the discrete‐time case. First, a tuning parameter is introduced to construct the proportional‐integral‐derivative control framework of positive systems. The proportional‐integral‐derivative control problem is thus transformed into the control synthesis of positive systems with time‐delay. By virtue of a matrix decomposition approach, the gain matrices of proportional‐integral‐derivative controller are designed. Using co‐positive Lyapunov functions and linear programming, the positivity and stability of positive systems are achieved under the designed proportional‐integral‐derivative controller. Then, the proportional‐integral‐derivative control of positive systems with reference signal is solved. The main contribution of this paper lies in that linear programming associated to the matrix decomposition approach is first presented to handle the proportional‐integral‐derivative control of positive systems. Finally, two illustrative examples are provided to verify the effectiveness of the proposed design.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Linear programming‐based proportional‐integral‐derivative control of positive systems

Zhou

Zhang

Jia

et al. 2023

IET Control Theory & Appl

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“…E( t ) is the system control error, and the calculation formula is as follows [ 30 , 31 ]:

where the r ( t ) is the expected value of the variable; c ( t ) is the actual value of the system.…”

Section: Monitoring and Control Principlementioning

confidence: 99%

A Sport Monitoring System Based on the Optimized Adaptive Fuzzy PID Control Algorithm in OneNet Internet of Things and Cloud Platform

Zhang

Min

2022

Computational Intelligence and Neuroscience

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Aiming at some problems existing in the existing sports monitoring system, based on the joint action of OneNet Internet of Things (IoT) and cloud platform, an optimized adaptive fuzzy PID control algorithm is adopted to monitor and analyze sports. Finally, the accuracy of the optimization model is verified through the comparison of different models, and the algorithm is used to predict and analyze sports. The research shows that (1) as the index calculation of cloud platform shows, with the increase of iteration time, the change curve of relevant indexes can be divided into four different stages, namely, rapid fluctuation stage, slow decline stage, slow fluctuation stage, and rapid decline stage. (2) The conventional calculation method (CPID) cannot well describe the change rule of the test data in the early stage of settlement. The fuzzy adaptive calculation method (NPID) also exposes some errors in the fitting and description of the test curve in the calculation process, while the improved adaptive calculation method (GPID) can describe the change characteristics and rules of the test curve well for different stages. (3) Compared with the original model, the optimization model can better describe the first and second stages of index change, indicating the accuracy of the optimization model. And the algorithm can be used to predict and analyze the changes of indicators and sports monitoring better, and the analysis results can provide relevant guidance for sports monitoring. This optimization scheme provides basis and theoretical support for the application of OneNet IoT and cloud platform.

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$$H_{\infty }$$ Filter Design for Discrete-Time Switched Interconnected Systems with Time Delays

Arthi,

Antonyronika

2023

Studies in Systems, Decision and Control

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Robust finite-time PID control for discrete-time large-scale interconnected uncertain system with discrete-delay

Cited by 14 publications

References 29 publications

Linear programming‐based proportional‐integral‐derivative control of positive systems

Linear programming‐based proportional‐integral‐derivative control of positive systems

A Sport Monitoring System Based on the Optimized Adaptive Fuzzy PID Control Algorithm in OneNet Internet of Things and Cloud Platform

$$H_{\infty }$$ Filter Design for Discrete-Time Switched Interconnected Systems with Time Delays

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